Visualization of relationships and strengths between data nodes

ABSTRACT

One or more processors receive a dataset that includes a plurality of nodes. One or more processors identify relationships between a plurality of interacting nodes within the dataset. One or more processors determine relationship strength values between a plurality of interacting node pairs within the dataset. One or more processors generate a graphical representation that represents the relationship strength values between the plurality of interacting nodes within the dataset. Interacting node pairs are connected by edges and the edges have a length that correlates with the relationship strength value between the interacting node pairs.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of data analysis,and more particularly to the visualization of relationship strengthsover time.

Data visualization is a modern branch of descriptive statistics. Itinvolves the creation and study of the visual representation of data.The main goal of data visualization is to communicate informationclearly and effectively through graphical means. This goal becomes morechallenging as datasets become larger. Such challenges are oftenencountered by organizations that analyze extremely large datasets, suchas data containing financial records, email correspondence, and socialnetworks. To visualize such large amounts of data, these organizationsoften require graphical tools to generate visual models of the data thatare intuitively understandable for users.

SUMMARY

Embodiments of the present invention provide a method, system, andprogram product for visualizing relationships and relationship strengthsof data. One or more processors receive a dataset that includes aplurality of nodes. One or more processors identify relationshipsbetween a plurality of interacting nodes within the dataset. One or moreprocessors determine relationship strength values between a plurality ofinteracting node pairs within the dataset. One or more processorsgenerate a graphical representation that represents the relationshipstrength values between the plurality of interacting nodes within thedataset, wherein interacting node pairs are connected by edges and theedges have a length that correlates with the relationship strength valuebetween the interacting node pairs.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a data analysisenvironment, in accordance with an exemplary embodiment of the presentinvention.

FIG. 2 illustrates operational processes of a relationship and strengthgraphing (RSG) program, executing on a computing device within theenvironment of FIG. 1, in accordance with an exemplary embodiment of thepresent invention.

FIGS. 3 and 4 are pictorial representations illustrating an end-userutilizing exemplary embodiments of the invention.

FIG. 5 depicts a block diagram of components of the computing deviceexecuting the RSG program, in accordance with an exemplary embodiment ofthe present invention.

DETAILED DESCRIPTION

Force-directed graph drawing algorithms assign forces among the set ofedges and the set of nodes of a graph drawing. Typically, spring-likeattractive forces based on Hooke's law are used to attract pairs ofendpoints of the graph's edges towards each other, while simultaneouslyrepulsive forces like those of electrically charged particles based onCoulomb's law are used to separate all pairs of nodes. In equilibriumstates for this system of forces, the edges tend to have uniform length.

Embodiments of the present invention provide a visualizing graphicaltool that represents an improvement in the field of large-dataanalytics. Embodiments of the present invention recognize that extremelylarge datasets are difficult to visualize with current force-directedalgorithms. Embodiments of the present invention provide intuitivevisualization of data by correlating data relationship strength with thepositioning of graph vertices representing that data. Embodiments of thepresent invention recognize that the length of graph edges will impartimportant visual meaning to large datasets. Embodiments of the presentinvention provide an approach to visualize endlessly growing datasetsinvolving related data points. Embodiments of the present inventionvisually animate the change in relationship strengths between datapoints in large datasets over time.

The present invention will now be described in detail with reference tothe Figures.

FIG. 1 is a functional block diagram illustrating a data analysisenvironment, generally designated 100, in accordance with one embodimentof the present invention. Data analysis environment 100 includescomputing device 110 connected over network 130. Computing device 110includes relationship and strength graphing (RSG) program 120 andvisualization data 125.

In various embodiments of the present invention, computing device 110 isa computing device that can be a standalone device, a server, a laptopcomputer, a tablet computer, a netbook computer, a personal computer(PC), or a desktop computer. In another embodiment, computing device 110represents a computing system utilizing clustered computers andcomponents to act as a single pool of seamless resources. In general,computing device 110 can be any computing device or a combination ofdevices with access to RSG program 120 and visualization data 125 and iscapable of executing RSG program 120. Computing device 110 may includeinternal and external hardware components, as depicted and described infurther detail with respect to FIG. 5.

In this exemplary embodiment, RSG program 120 and visualization data 125are stored on computing device 110. However, in other embodiments, RSGprogram 120 and visualization data 125 may be stored externally andaccessed through a communication network, such as network 130. Network130 can be, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, or a combination of the two, and may includewired, wireless, fiber optic or any other connection known in the art.In general, network 130 can be any combination of connections andprotocols that will support communications between computing device 110,RSG program 120, and visualization data 125, in accordance with adesired embodiment of the present invention.

In an embodiment, visualization data 125 is any data capable of beingvisually expressed in graphs containing nodes with edges connecting thenodes, where the relationships between the nodes are of varyingstrengths. In one embodiment, the graph is a directed graph. In yetanother embodiment, the graph is an undirected graph. An individual datarecord for visualization data 125 requires labels for the nodes and astrength-of-relationship value between nodes that are connected byedges. Example embodiments of visualization data 125 include emails,social networks, and even financial transactions. In the case of emails,example embodiments for nodes are email sender and receiver. Otherexample embodiments for nodes are locations. For example, money wiredfrom London to New York is visualized as two nodes labeled “London” and“New York”. The strength-of-relationship value shows the importance ofthe individual record or transaction. The strength-of-relationship valueis a numeric value that is assigned or calculated. In one embodiment,the importance that was assigned to an email by the sender is given acertain strength-of-relationship value. For example, an email of highimportance is given a value of “1” while two other emails that were ofmedium and low importance are given values of “0.67” and “0.33”,respectively. In another embodiment, the number of emails exchangedbetween a given pair of individuals is used to calculate the importanceof their relationship. For example, ten emails exchanged between twoparties in a given week shows a stronger relationship than two emailsexchanged in the same week. In yet another embodiment, both thefrequency and assigned importance of a transaction is combined to createa strength-of-relationship value.

In exemplary embodiments, RSG program 120 creates graphs that indicatestrength of relationship between nodes by varying the length of theedges that connect them. In one embodiment, RSG program 120 shows theresulting graph in a static mode. In another embodiment, RSG program 120shows the resulting graph in an animated display. In one embodiment, RSGprogram 120 utilizes a modified force-directed algorithm. In anotherembodiment, a separate layout algorithm that uses a constraint-basedgradual displacement of nodes is employed.

FIG. 2 illustrates operational processes, 200, of RSG program 120,executing on computing device 110 within data analysis environment 100,in accordance with an exemplary embodiment of the present invention. Inone embodiment, RSG program 120 receives data in the form of record setsin step 205. The record sets could be, for example, transactional datasuch as emails, financial records, and social networks. In step 210, RSGprogram 120 identifies the transactional record's nodes. As mentionedabove, RSG program 120 identifies email sender and receiver as nodelabels in one example. In another example, transactional data betweentwo different locations would label the nodes with those location names.

In steps 215 and 220, RSG program 120 identifies the total number ofdata points in the record and determines relationship strength,respectively. For example, some transactional records may be 3-tuples,which have two nodes and a value for strength-of relationship. Anexample of a 3-tuple record would be an email record which includes asender, receiver, and a numeric level of importance. The sender would beone node, the receiver would be a second node, and the numeric level ofimportance would be the strength-of-relationship. Embodiments of thepresent invention also accommodate records that contain more than threedata points. For example, email records that include sender, receiver,numeric level of importance, and a number of emails exchanged would be a4-tuple record. In this embodiment, the sender and receiver are againnodes, but the numeric level of importance would be combined with thenumber of emails to generate a strength-of-relationship value. In oneembodiment, the strength-of-relationship value is an expression ofaverage strength. In another embodiment, the strength-of-relationshipvalue the overall sum of combined strength.

In step 225 RSG program 120 generates a graph with nodes connected byedges of varying lengths. The edge lengths correlate with and provide avisualization of relationship strength. An exemplary embodiment showsedge length becoming shorter as relationship strength becomes stronger.The average strength-of-relationship (s_(avg)) of the records isdetermined and used in the following equation:

$\begin{matrix}{{l\left( e_{i} \right)} = {L \cdot \frac{1.0}{0.5 + e^{- {({5 \cdot {({s_{i} - s_{avg}})}})}}}}} & (1)\end{matrix}$Where l(e_(i)) is the edge length for any graph edge corresponding torecord i; L is a scaling factor specific to the visualization and chosenby the user; and s_(i) is the numeric strength-of-relationship for anyrecord i. RSG program 120 generates an output graph where the lengths ofthe edges connecting related node pairs is based on the relativestrength of their relationships as determined in Equation 1 above.

In another exemplary embodiment of step 225, RSG program 120 depictsanimation showing how relationship strengths between nodes change overtime for a given time window. In this embodiment, the change of edgelength, d(e_(i)), is calculated by the following equation:

$\begin{matrix}{{d\left( e_{i} \right)} = {\left( \frac{s_{i} - s_{avg}}{{s_{i} - s_{avg}}} \right)\frac{L}{2}\sqrt{{s_{i} - s_{avg}}}}} & (2)\end{matrix}$Where l(e_(i)) is the edge length for any graph edge corresponding torecord i; L is a scaling factor specific to the visualization and chosenby the user; and s_(i) is the numeric strength-of-relationship for anyrecord i. Using equation 2, RSG program 120 incrementally shows thestretching and contracting of edge lengths for user-defined timeincrements within the time window.

FIG. 3 shows an exemplary embodiment, 300, of the present invention.FIG. 3 displays one example of the graphics disclosed in step 225 ofFIG. 2. The example in FIG. 3 shows the effect of visualizingrelationship strengths for a four edge set, {AB, BC, BD, BE},representing five nodes and four records. Screenshot 310 shows aforce-directed graph wherein relationship strength has not beencalculated for the record set and relationship strength is thereforeunknown. Screenshot 320 shows a graph generated by RSG program 120showing the strengths between the nodes using edge length. Therelationship between nodes A and B are the strongest and the one betweenB and C is the weakest. The BD and BE relationships are roughly equaland in-between AB and BC in terms of strength.

In one embodiment, visualization of the animation allows trends to beidentified. For example, relationships between nodes are visualized tohave a cyclic or perhaps a seasonal nature of strengthening andweakening when viewed over an extended period of time. In yet anotherembodiment, visualization of the animation allows predictions of futurerelationship strengths to be estimated or calculated. For example,relationship strength between business associates exchanging emails arepredicted to increase or decrease towards the end of a quarter or fiscalyear.

An example of trend-spotting over time is seen in embodiment 400 shownin FIG. 4. In this example of an animated graph, the relationshipstrength is visualized over the course of a nearly two years inquarterly windows between data points A, B, C, D, and E. Screenshot 405shows that relationships AB, BC, BD, and BE are similar in strengthbetween Jan. 1 and Mar. 31, 2013 (Q1 2013). Between April 1^(st) andJune 30th (screenshot 410, Q2 2013), the AB relationship is strongerthan in Q1 2013, the BC relationship strength is weaker while BD and BEdo not appear to have changed at all. Between July 1^(st) and September30th (screenshot 415, Q3 2013), the BC and AB relationships are similarto their strengths in Q1, BD remains unchanged and BE has is weaker thanany previous quarter. By the end of the year (screenshot 420, Q4 2013),AB appears to be as strong as its Q2 strength, which is similar instrength to the relationship strength between B and C. The BD and BEstrengths are similar to Q3 2013.

Examination of the first three quarters of 2014 (screenshots 425, 430,and 435) show an analogous trend of strength values for the fourrelationships. Q1 2014 (screenshot 425) appears quite similar to Q1 2013(screenshot 405), Q2 2014 (screenshot 430) appears almost identical toQ2 2013 (screenshot 410), and Q3 2014 (screenshot 435) closely mirrorsQ3 2013 (screenshot 415). This leads to the prediction that Q4 2014 willmimic what was observed for Q4 2013. Thus, the user predicts screenshot440 will be the graph seen for Q4 2014 because it is identical to thegraph seen for Q4 2013 (screenshot 420).

In some embodiments, RSG program 120 includes statistical sub-programsthat provide RSG program 120 with the functionality to perform variousstatistical analysis operations on data. For example, in FIG. 4, RSGprogram 120 uses such a sub-program to identify the trends, patternsetc. in the changes in strength between nodes A, B, C, D and E. RSGprogram 120 then uses that sub-program to predict what the futurestrength will be between those nodes (shown in screenshot 440). In someembodiments, such an analysis and prediction include a degree ofconfidence based on how far into the future the prediction isdetermined. For example, if the data that is analyzed spans eight yearswith five hundred data-points and the prediction is for a point that isthree months into the future, then the degree of confidence is “high”.In certain embodiments, the difference between the time period overwhich data is gathered and the time span between the last data point andthe time of the prediction impacts the degree of confidence associatedwith a prediction. In continuation with the previous example, aprediction for a point that is three years into the future would yield adegree of confidence is “low” because the degree of confidence decreasesthe father into the future the prediction is made and the data was onlygathered over eight years. In other embodiments, the number ofdata-points impacts the degree of confidence associated with aprediction. Like most forms of statistical analysis, a larger number ofdata points over a given time period increases the degree of confidenceassociated with a prediction, at least to a point.

FIG. 5 depicts a block diagram, 500, of components of computing device100, in accordance with an illustrative embodiment of the presentinvention. It should be appreciated that FIG. 5 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environment may be made.

Computing device 110 includes communications fabric 502, which providescommunications between computer processor(s) 504, memory 506, persistentstorage 508, communications unit 510, and input/output (I/O)interface(s) 512. In one embodiment, communications fabric 502 isimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,communications fabric 502 can be implemented with one or more buses.

Memory 506 and persistent storage 508 are computer-readable storagemedia. In this embodiment, memory 506 includes random access memory(RAM) 514 and cache memory 516. In general, memory 506 can include anysuitable volatile or non-volatile computer-readable storage media.

Visualization data 125, and RSG program 120 are stored in persistentstorage 508 for execution and/or access by one or more of the respectivecomputer processors 504 via one or more memories of memory 506. In thisembodiment, persistent storage 508 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 508 can include a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer-readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 508 may also be removable. Forexample, a removable hard drive may be used for persistent storage 508.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage508.

Communications unit 510, in these examples, provides for communicationswith other data processing systems or devices, including resources ofnetwork 130. In these examples, communications unit 510 includes one ormore network interface cards. Communications unit 510 may providecommunications through the use of either or both physical and wirelesscommunications links. Visualization data 125, and RSG program 120 may bedownloaded to persistent storage 508 through communications unit 510.

I/O interface(s) 512 allows for input and output of data with otherdevices that may be connected to computing device 110. For example, I/Ointerface 512 may provide a connection to external devices 518 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 518 can also include portable computer-readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention, e.g., Visualization data 125, andRSG program 120, can be stored on such portable computer-readablestorage media and can be loaded onto persistent storage 508 via I/Ointerface(s) 512. I/O interface(s) 512 also connect to a display 520.

Display 520 provides a mechanism to display data to a user and may be,for example, a computer monitor, or a television screen.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

It is to be noted that the term(s) “Smalltalk” and the like may besubject to trademark rights in various jurisdictions throughout theworld and are used here only in reference to the products or servicesproperly denominated by the marks to the extent that such trademarkrights may exist.

What is claimed is:
 1. A method for visualizing relationships andrelationship strengths of data comprising: receiving, by one or moreprocessors, a dataset that includes a plurality of nodes; identifying,by one or more processors, relationships between a plurality ofinteracting nodes within the dataset; determining, by one or moreprocessors, relationship strength values between a plurality ofinteracting node pairs within the dataset, wherein a user, representedby a node, actively assigns an importance value to each interaction theuser participates in, wherein the importance values are taken intoconsideration along with a frequency of interactions to calculate astrength-of-relationship value; generating, by one or more processors, agraphical representation that represents the relationship strengthvalues between the plurality of interacting nodes within the dataset,wherein interacting node pairs are connected by edges, wherein adistance between the interacting node pairs provides a correlation withthe relationship strength value between the interacting node pairs, andwherein a calculation to determine the distance between the interactingnode pairs includes an equation: $\begin{matrix}{{l\left( e_{i} \right)} = {L \cdot \frac{1.0}{0.5 + e^{- {({5 \cdot {({s_{i} - s_{avg}})}})}}}}} & (1)\end{matrix}$ where l(e_(i)) is a length of an edge corresponding to arecord i; L is a scaling factor specific to a visualization and chosenby a user; s_(i) is a numeric strength-of-relationship for the record i;and s_(avg) is an average numeric strength-of-relationship; displaying,by one or more processors, the graphical representation to a user,wherein the graphical representation depicts the relationship strengthvalues between the plurality of interacting nodes within the datasetsuch that the relationship strength values between the plurality ofinteracting node pairs are conveyed to the user; counting, by one ormore processors, a number of interactions between interacting nodepairs; increasing, by one or more processors, the relationship strengthvalue based, at least in part, on each interaction counted; andgenerating, by one or more processors, an animation showing a change inrelationship strength over time using a plurality of graphicalrepresentations that represent a specified time period.
 2. The method ofclaim 1 further comprising: predicting, by one or more processors, afuture relationship strength value for at least one of the interactingnode pairs; and providing, by one or more processors, a degree ofconfidence value for the future relationship strength value based, atleast in part, on a statistical analysis of data included in thedataset.
 3. The method of claim 1 further comprising: generating, by oneor more processors, a graphical representation that includes avisualization of one or both of trends and patterns in a change ofrelationship strength between interacting nodes.
 4. The method of claim1 further comprising: generating, by one or more processors, thegraphical representations using a modified force-directed algorithm. 5.The method of claim 1 further comprising: generating, by one or moreprocessors, the graphical representations using a separate layoutalgorithm.
 6. A computer program product for visualizing relationshipsand relationship strengths of data comprising: one or morecomputer-readable storage media and program instructions stored on theone or more computer-readable storage media, the program instructionscomprising: program instructions to receive a dataset that includes aplurality of nodes; program instructions to identify relationshipsbetween a plurality of interacting nodes within the dataset, wherein auser, represented by a node, actively assigns an importance value toeach interaction the user participates in, wherein the importance valuesare taken into consideration along with a frequency of interactions tocalculate a strength-of-relationship value; program instructions todetermine relationship strength values between a plurality ofinteracting node pairs within the dataset; program instructions togenerate a graphical representation that represents the relationshipstrength values between the plurality of interacting nodes within thedataset, wherein interacting node pairs are connected by edges, whereina distance between the interacting node pairs provides a correlationwith the relationship strength value between the interacting node pairs,and wherein a calculation to determine the distance between theinteracting node pairs includes an equation: $\begin{matrix}{{l\left( e_{i} \right)} = {L \cdot \frac{1.0}{0.5 + e^{- {({5 \cdot {({s_{i} - s_{avg}})}})}}}}} & (1)\end{matrix}$ where l(e_(i)) is a length of an edge corresponding to arecord i; L is a scaling factor specific to a visualization and chosenby a user; s_(i) is a numeric strength-of-relationship for the record i;and s_(avg) is an average numeric strength-of-relationship; programinstructions to display the graphical representation to a user, whereinthe graphical representation depicts the relationship strength valuesbetween the plurality of interacting nodes within the dataset such thatthe relationship strength values between the plurality of interactingnode pairs are conveyed to the user program instructions to count anumber of interactions between interacting node pairs; programinstructions to increase the relationship strength value based, at leastin part, on each interaction counted; and program instructions togenerate an animation showing a change in relationship strength overtime using a plurality of graphical representations that represent aspecified time period.
 7. The computer program product of claim 6further comprising: program instructions to predict a futurerelationship strength value for at least one of the interacting nodepairs; and program instructions to provide a degree of confidence valuefor the future relationship strength value based, at least in part, on astatistical analysis of data included in the dataset.
 8. The computerprogram product of claim 6 further comprising: program instructions togenerate a graphical representation that includes a visualization of oneor both of trends and patterns in a change of relationship strengthbetween interacting nodes.
 9. The computer program product of claim 6further comprising: program instructions to generate the graphicalrepresentations using a modified force-directed algorithm.
 10. Thecomputer program product of claim 6 further comprising: programinstructions to generate the graphical representations using a separatelayout algorithm.
 11. A computer system for visualizing relationshipsand relationship strengths of data comprising: one or more computerprocessors; one or more computer readable storage medium; programinstructions stored on the computer readable storage medium forexecution by at least one of the one or more processors, the programinstructions comprising: program instructions to receive a dataset thatincludes a plurality of nodes; program instructions to identifyrelationships between a plurality of interacting nodes within thedataset; program instructions to determine relationship strength valuesbetween a plurality of interacting node pairs within the dataset,wherein a user, represented by a node, actively assigns an importancevalue to each interaction the user participates in, wherein theimportance values are taken into consideration along with a frequency ofinteractions to calculate a strength-of-relationship value; programinstructions to generate a graphical representation that represents therelationship strength values between the plurality of interacting nodeswithin the dataset, wherein interacting node pairs are connected byedges, wherein a distance between the interacting node pairs provides acorrelation with the relationship strength value between the interactingnode pairs, and wherein a calculation to determine the distance betweenthe interacting node pairs includes an equation: $\begin{matrix}{{l\left( e_{i} \right)} = {L \cdot \frac{1.0}{0.5 + e^{- {({5 \cdot {({s_{i} - s_{avg}})}})}}}}} & (1)\end{matrix}$ where l(e_(i)) is a length of an edge corresponding to arecord i; L is a scaling factor specific to a visualization and chosenby a user; s_(i) is a numeric strength-of-relationship for the record i;and s_(avg) is an average numeric strength-of-relationship; programinstructions to display the graphical representation to a user, whereinthe graphical representation depicts the relationship strength valuesbetween the plurality of interacting nodes within the dataset such thatthe relationship strength values between the plurality of interactingnode pairs are conveyed to the user program instructions to count anumber of interactions between interacting node pairs; programinstructions to increase the relationship strength value based, at leastin part, on each interaction counted; and program instructions togenerate an animation showing a change in relationship strength overtime using a plurality of graphical representations that represent aspecified time period.
 12. The computer system of claim 11, the programinstructions further comprising: program instructions to predict afuture relationship strength value for at least one of the interactingnode pairs; and program instructions to provide a degree of confidencevalue for the future relationship strength value based, at least inpart, on a statistical analysis of data included in the dataset.
 13. Thecomputer system of claim 11, the program instructions furthercomprising: program instructions to generate a graphical representationthat includes a visualization of one or both of trends and patterns in achange of relationship strength between interacting nodes.
 14. Thecomputer system of claim 11, the program instructions furthercomprising: program instructions to generate the graphicalrepresentations using a modified force-directed algorithm.